~6,000 barcoded TP53 reporters were probed in MCF7 TP53WT/KO cells and stimulated with Nutlin-3a. I previously processed the raw sequencing data, quantified the pDNA data and normalized the cDNA data. In this script, a detailed dissection of the reporter activities will be carried out to understand how TP53 drives transcription and to identify the most sensitive TP53 reporters.
Aim: I want to characterize the reporter activity distributions in
the tested conditions. Does Nutlin boost P53 reporter activity and is
P53 inactive in the KO cells?
## [1] 0.9685877
## [1] 0.9036356
## [1] 0.903232
Conclusion: 1F: Replicates do correlate well. 1G: Negative controls are inactive compared to P53 reporters. P53 reporters become more active in WT cells and even more active upon Nutlin stimulation.
Aim: How does the binding site affinity, copy number, and their respective positioning affect reporter activity?
Conclusion: BS006 is the most responsive to Nutlin-3a. Addition of binding sites is super-additive. Positioning of binding sites matters - putting them directly next to each other is inhibitory, and putting them close to the TSS leads to higher activity.
Figure 3: The effect of the spacer length.
Aim: Show how the spacer length between adjacent binding sites affects reporter activity.
Conclusion: Spacer length influences activity periodically. Adjacent binding sites need to be 180 degrees tilted with respect to each other to achieve optimal activation.
Aim: Show how the P53 reporters interact with the two minimal promoters and the three spacer sequences.
Conclusion: Promoter and spacer sequence influence activity linearly.
Aim: Can we explain now every observation using a linear model?
## MODEL INFO:
## Observations: 264
## Dependent Variable: log2(reporter_activity)
## Type: OLS linear regression
##
## MODEL FIT:
## F(9,254) = 109.03, p = 0.00
## R² = 0.79
## Adj. R² = 0.79
##
## Standard errors: OLS
## ---------------------------------------------------------------
## Est. S.E. t val. p
## -------------------------------- ------- ------ -------- ------
## (Intercept) 4.01 0.08 50.79 0.00
## promotermCMV 0.67 0.09 7.39 0.00
## background2 -0.91 0.09 -10.03 0.00
## background3 0.33 0.09 3.57 0.00
## spacing_degree_transf 0.75 0.04 20.06 0.00
## affinity_id3_med_only 0.13 0.07 1.75 0.08
## affinity_id5_low_only 0.89 0.07 11.97 0.00
## affinity_id7_very-low_only 0.82 0.07 11.06 0.00
## promotermCMV:background2 0.44 0.13 3.45 0.00
## promotermCMV:background3 -0.76 0.13 -5.92 0.00
## ---------------------------------------------------------------
## MODEL INFO:
## Observations: 264
## Dependent Variable: log2(reporter_activity)
## Type: OLS linear regression
##
## MODEL FIT:
## F(9,254) = 88.29, p = 0.00
## R² = 0.76
## Adj. R² = 0.75
##
## Standard errors: OLS
## ---------------------------------------------------------------
## Est. S.E. t val. p
## -------------------------------- ------- ------ -------- ------
## (Intercept) 2.78 0.10 28.58 0.00
## promotermCMV 1.11 0.11 9.92 0.00
## background2 -0.87 0.11 -7.79 0.00
## background3 0.50 0.11 4.46 0.00
## spacing_degree_transf 0.28 0.05 6.09 0.00
## affinity_id3_med_only -0.14 0.09 -1.50 0.13
## affinity_id5_low_only 1.43 0.09 15.57 0.00
## affinity_id7_very-low_only -0.08 0.09 -0.90 0.37
## promotermCMV:background2 0.21 0.16 1.31 0.19
## promotermCMV:background3 -1.13 0.16 -7.10 0.00
## ---------------------------------------------------------------
Conlusion: Top reporters are better than commercial reporters. Linear model gives insights into which features are important to drive high expression.
paste("Run time: ",format(Sys.time()-StartTime))## [1] "Run time: 40.65543 secs"
getwd()## [1] "/DATA/usr/m.trauernicht/projects/P53_reporter_scan/analyses"
date()## [1] "Thu Apr 20 09:56:23 2023"
sessionInfo()## R version 4.0.5 (2021-03-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.6 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] scales_1.2.0 ggrastr_1.0.1 jtools_2.1.4
## [4] glmnetUtils_1.1.8 glmnet_4.1-4 Matrix_1.5-1
## [7] randomForest_4.6-14 plotly_4.10.0 ROCR_1.0-11
## [10] tidyr_1.2.0 stringr_1.4.0 readr_2.1.2
## [13] GGally_2.1.2 gridExtra_2.3 cowplot_1.1.1
## [16] plyr_1.8.7 viridis_0.6.2 viridisLite_0.4.0
## [19] ggforce_0.3.3 ggbeeswarm_0.6.0 ggpubr_0.4.0
## [22] pheatmap_1.0.12 tibble_3.1.6 maditr_0.8.3
## [25] dplyr_1.0.8 ggplot2_3.4.0 RColorBrewer_1.1-3
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-152 httr_1.4.2 tools_4.0.5 backports_1.4.1
## [5] bslib_0.3.1 utf8_1.2.2 R6_2.5.1 vipor_0.4.5
## [9] mgcv_1.8-34 DBI_1.1.2 lazyeval_0.2.2 colorspace_2.0-3
## [13] withr_2.5.0 tidyselect_1.1.2 compiler_4.0.5 cli_3.4.1
## [17] Cairo_1.5-15 labeling_0.4.2 sass_0.4.1 digest_0.6.29
## [21] rmarkdown_2.13 pkgconfig_2.0.3 htmltools_0.5.2 highr_0.9
## [25] fastmap_1.1.0 htmlwidgets_1.5.4 rlang_1.0.6 rstudioapi_0.13
## [29] shape_1.4.6 jquerylib_0.1.4 farver_2.1.0 generics_0.1.2
## [33] jsonlite_1.8.0 car_3.0-12 magrittr_2.0.3 Rcpp_1.0.8.3
## [37] munsell_0.5.0 fansi_1.0.3 abind_1.4-5 lifecycle_1.0.3
## [41] stringi_1.7.6 yaml_2.3.5 carData_3.0-5 MASS_7.3-53.1
## [45] grid_4.0.5 parallel_4.0.5 crayon_1.5.1 lattice_0.20-41
## [49] splines_4.0.5 pander_0.6.5 hms_1.1.1 knitr_1.38
## [53] pillar_1.7.0 ggsignif_0.6.3 codetools_0.2-18 glue_1.6.2
## [57] evaluate_0.15 data.table_1.14.2 vctrs_0.5.1 tzdb_0.3.0
## [61] tweenr_1.0.2 foreach_1.5.2 gtable_0.3.0 purrr_0.3.4
## [65] polyclip_1.10-0 reshape_0.8.9 assertthat_0.2.1 xfun_0.30
## [69] broom_0.8.0 rstatix_0.7.0 survival_3.2-10 iterators_1.0.14
## [73] beeswarm_0.4.0 ellipsis_0.3.2